Stop Clicking Buttons: Automate Gemini Fine-Tuning with GitHub Actions
The Google Cloud Console is great for exploration. But when you're experimenting with Gemini fine-tuning, manual clicking becomes a bottleneck.
If you need to iterate on training data—testing different examples, adjusting epochs, tuning hyperparameters—clicking through the Console UI to upload datasets and launch training jobs doesn't scale. Do it once? Fine. Do it ten times while iterating? Painful.
The solution: a fire-and-forget MLOps pipeline. Push a new training.jsonl to GitHub, and the rest happens automatically—data upload, job submission, training. No clicks, no manual API calls, just git push.
Why Automate Fine-Tuning?
Fine-tuning Gemini models can improve performance on domain-specific tasks, but the iteration process is tedious when done manually. Experimenting with different training datasets, epoch counts, and hyperparameters requires multiple training runs. Automating this workflow lets you iterate quickly without manual Console work—whether you're testing fine-tuning feasibility or running production jobs.